Title:  Utilities for Mountain Pine Beetle Simulation Modelling 

Description:  Utilities for mountain pine beetle (MPB) simulation modelling. 
Authors:  Eliot J B McIntire [aut, cre] , Alex M Chubaty [aut] 
Maintainer:  Eliot J B McIntire <[email protected]> 
License:  GPL3 
Version:  0.1.3.9002 
Built:  20240915 06:02:58 UTC 
Source:  https://github.com/PredictiveEcology/mpbutils 
This is for converting forecasted abundance stacks in a list of simList objects into a list of binary stacks (presence/absence)
This is for converting forecasted abundance stack in a simList object into a binary stack (presence/absence)
The mask will occur with values of NA or values of 0.
binaryStacks( sims, thresholdAttackTreesMinDetectable = 1.4, thresholdPineProportion = 0.3, stackName = "predictedStack", propPineRasName = "propPineRas" ) binaryStack( stk, propPineRas, thresholdAttackTreesMinDetectable = 1.4, thresholdPineProportion = 0.3 ) maskWPine(ras, propPineRas, thresholdPineProportion)
binaryStacks( sims, thresholdAttackTreesMinDetectable = 1.4, thresholdPineProportion = 0.3, stackName = "predictedStack", propPineRasName = "propPineRas" ) binaryStack( stk, propPineRas, thresholdAttackTreesMinDetectable = 1.4, thresholdPineProportion = 0.3 ) maskWPine(ras, propPineRas, thresholdPineProportion)
sims 
a list of simLists 
thresholdAttackTreesMinDetectable 
A scalar that delineates presence from an absense. Default is 1.4, which was derived from initial efforts to find a single value that works in all years. 
thresholdPineProportion 
A scalar. Values on the propPineRas that are below this threshold will be masked out, i.e., set to NA. 
stackName 
Character string. The name of the stack inside the simLists to use 
propPineRasName 
Character string. The name of the RasterLayer inside the simLists to use for proportion Pine. 
stk 
A stack of abundance 
propPineRas 
A RasterLayer that has values between 0 and 1, representing the proportion of pine cover in the pixel. 
ras 
A RasterLayer of abundance 
Cleans the predicted raster
cleanUpPredictionRas( rasLog, propPineRas, thresholdAttackTreesMinDetectable = 1.4, thresholdPineProportion = 0.3 )
cleanUpPredictionRas( rasLog, propPineRas, thresholdAttackTreesMinDetectable = 1.4, thresholdPineProportion = 0.3 )
rasLog 
A RasterLayer of predicted mass attack, on a log scale 
propPineRas 
A RasterLayer of proportion pine cover. 
thresholdAttackTreesMinDetectable 
A scalar. This will have come from
an 
thresholdPineProportion 
A scalar. The proportion of pine 
years 
A scalar indicating how many years are included in the 
A RasterLayer
with ever
Calculate cumulative sum of a stack, with optional log
cumulativeMap(stk, log = TRUE)
cumulativeMap(stk, log = TRUE)
stk 
a RasterStack 
log 
Logical. Will take 
From Cooke and Carroll (2017).
growthFunction(x, s, dataset, growthData)
growthFunction(x, s, dataset, growthData)
x 
beetle density 
s 
climate suitability factor 
dataset 
dataset name used for fitting:
one of 
growthData 
MPB red top growth data with which to fit. 
This is a 2dimensional tdistribution that can be used for dispersal of natural entities. It has many characteristics (see Clark et al 1999)
kernel_twoDT(dist, mu, p) kernel_twoDT_mean(mu, m)
kernel_twoDT(dist, mu, p) kernel_twoDT_mean(mu, m)
dist 
A vector of distances 
mu 
The first parameter of the 2Dt kernel. This represents about 0.9 of the mean dispersal distance 
p 
The second parameter of the 2Dt kernel. This changes the shape. 
A vector of probabilities
Clark, J. S., M. Silman, R. Kern, E. Macklin, and J. HilleRisLambers. 1999. Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80:1475–1494.
Create study area based o ecoregion selection
mpbStudyArea(ecoregions = c(112, 120, 122, 124, 126), targetCRS, cPath, dPath)
mpbStudyArea(ecoregions = c(112, 120, 122, 124, 126), targetCRS, cPath, dPath)
ecoregions 
numeric vector indicating which ecoregions to be included as part of the study area. Derived from http://sis.agr.gc.ca/cansis/nsdb/ecostrat/region/ecoregion_shp.zip. 
targetCRS 
target CRS string to use for reprojecting ecodistrict (study area) polygons. 
cPath 
cache path 
dPath 
destination path 
an sf
object
From Cooke & Carroll (2017).
xt(xtminus1, cs, dataset, massAttacksMap, growthData)
xt(xtminus1, cs, dataset, massAttacksMap, growthData)
xtminus1 
previous year's attack density 
cs 
climate suitability factor 
dataset 
dataset name used for fitting:
one of 
massAttacksMap 

growthData 
MPB red top growth data with which to fit. 